CN1208025C - Method and device for evaluating an kinetic pattern - Google Patents
Method and device for evaluating an kinetic pattern Download PDFInfo
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- CN1208025C CN1208025C CNB988138417A CN98813841A CN1208025C CN 1208025 C CN1208025 C CN 1208025C CN B988138417 A CNB988138417 A CN B988138417A CN 98813841 A CN98813841 A CN 98813841A CN 1208025 C CN1208025 C CN 1208025C
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
- A61B5/1127—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using markers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4005—Detecting, measuring or recording for evaluating the nervous system for evaluating the sensory system
- A61B5/4023—Evaluating sense of balance
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6814—Head
Abstract
A pattern is recorded by a plurality of mark generators (M <i>) which move along with measured person bodies for judging the movement pattern, particularly the movement of heads and the bodies of measured persons (1). Position curved lines (m <i>) of the mark generators (M <i>) are collected along with the time (t) in the three-dimensional space, are used as a data item (DF <i>) of a public data set (DS) of the mark generators (M <i>) and are stored. Parameters (KG) of the position curved lines (m <i>) are determined by a data processing device (3) according to the analysis of a computer, and are compared with a corresponding reference value (RG) by a pattern recognition method to judge. The judged movement pattern can be explained, and the information can provide the diagnostic conclusion of fundamental pathology diagrams which are mainly caused by spirit fault, somatopsychic fault and/or neural fault.
Description
The present invention relates to judge the method for kinetic pattern (kinetic pattern), this pattern is recorded by means of the marker generator (marker) that moves with measured's health in a large number.The invention still further relates to the related device of implementing the method.Here kinetic pattern is meant the pattern (skull-health figure (Carnio-Corpo-Gramm)) of head movement and trunk motion.
The equilibrium function fault is the main or sex expression as a result of many pathological examination results.This is related to chronic phenomenon on the one hand, as highly reduction, or the injury that when accident, causes, as the wrestling damage.A large amount of on the other hand diseases and balance are controlled restore or not recoverable fault relevant, comprise somatopsychic illness, as schizophrenia, mental disorder, depression and parkinson.
Equilibrated maintenance especially requires the regulation mechanism of high complexity for the people who stands, and also mainly contains search receiner (Tastrezeptor) and this adjusting of participation of various zones of eye, ear and human brain the organ of equilibration in internal ear (vestibular apparatus).In so-called " skull-health figure " work up method according to following knowledge: participate in the fault in the various zones of organ of balance control, it causes being examined a kinetic pattern that individual features is arranged of human body.Can be by observing kinetic pattern according to skull-health figure fault in the positioning balance control system by rule of thumb.Deducibility has the disease that the equilibrium function fault causes thus.
By the disclosed device of " skull-health figure research report " the ISBN 3-88383-126-3 of the publication in the editors and publishers of industry cooperative association (in June, 1986), by means of form is that the marker generator of electric filament lamp is made measured's head and trunk motion diagram reliably, is respectively putting one on measured's the both shoulders and on its volume and on the hindbrain.Each marker generator is arranged on measured's upper camera in the motion on the horizontal plane to be continued in the exposure as the optical track film recording in so-called skull-health figure by one.Optical track is manually judged on photo after test is finished.
Manually judge photo or pass through to measure how much optical tracks realizations, perhaps carry out correlative connection with the comparison pattern that is called " photo-unit " and realize that this need spend the long time by kinetic pattern with complexity.Its major defect is that the part of the information that produces is lost in test when the film recording sign moves.Only can recognition marks on photo the horizontal component of motion.It can not provide about indicating spatial moving both vertically and information highly.So, require comparison film to carry out the calibration of high cost in order to compare skull-health figure of the big or small measured of difference fully.Because the optical track of all signs is comprised on the photos, the frequent appearance owing to the tangent covering that causes of optical track, this makes feature of extracting single optical track become difficult or even may.Directly the information that takes place in the dead angle below video camera has also been lost, and video camera enters in the beam channel of advancing between reflecting mirror and marker generator there.
DE 38 29 885 C2 disclose a kind of device, wherein replace video camera to write down optical track with the photograph photovoltaic array that is installed in the measured top.Eliminated the dead angle by this array.By means of the digital computer basis optical track is analyzed in the calculating of skull-motion excursion that health figure is correlated with.Yet the judgement that the kinetic pattern of the collection that has no basis there describes.
The objective of the invention is to provide the method for a kind of judgement by means of the kinetic pattern of a plurality of marker generators records that move with measured's human body, the kinetic pattern that wherein contains extra high information density spends with the little time and is determined.Provide the device of a kind of particularly suitable of automatic enforcement this method in addition.
The present invention finishes like this about the task of providing a kind of method: in order to judge the method for one stature-trunk-kinetic pattern, this pattern is to write down by means of a plurality of marker generators that move with measured's human body, wherein, the position curve of each marker generator is collected in three dimensions in time, and be stored as a data item to the common data set of all marker generators, kinetic pattern is described by means of the parameter that is derived by data set, and each parameter compared with a reference value by the corresponding derivation of a stored reference data set, and parameter or each parameter are derived by the projection on the plane of at least one position curve in three datum planes of Cartesian Coordinate system.
The present invention examines the empirical value that conclusion obtains based on experience from following thinking in advance by the standard that skull-health figure can derive.Reliable diagnosis for based on the disease of the kinetic pattern of observing needs a plurality of reference tests, because the individual characteristic that comprises equally must be separated from the pathology characteristic in kinetic pattern.For the reliability of these knowledge and processed data volume has formed complexity, just conclusion is only possible with high time cost reliably.Yet the time-consuming judgement of single test makes it profitless in clinical practice.This also wishes in addition: the information dropout when the record single test is as much as possible little, and especially new requested knowledge is done judgement again to old research or test under neodoxy.By carrying out automatic pattern identification, can realize the detailed especially analysis of a kinetic pattern and the classification conclusion of acquisition motion morphology by means of computer or data handling equipment.These the possibility of result comprise the conclusion that occurs probably that promising later diagnosis is prepared from knowledge.
Owing to three-dimensional, the record of certain hour resolution is arranged, data set contains the full detail of test.Especially Biao Zhi position curve always can be separated from each other and judge.The time course of kinetic pattern is analyzed especially simply in addition.Based on the load capacity of modern data processing system, compare artificial judgement kinetic pattern and significantly reduced the time cost.
Parameter is derived by the projection of at least one position curve on a datum level of Cartesian Coordinate system.Opposite with WO 91/15148 disclosed decision method, neck rotation prismatic can be known by means of the space diagram of the head rotating shaft of being obtained by head movement graphics information technology and is expressed as a for example two-dimensional projection's position curve on fluorescent screen and being judged effectively.Because be used to diagnose or examine purpose in advance and the result of determination relevant with each process stage that form can show afterwards, so this is particularly advantageous.
Observed kinetic pattern typically presents periodic structure on skull-health figure, and it is to be swung by measured's human body to cause.Half wobble is corresponding to measured's a step in so-called walking test, and the measured finishes walking movement in this test.In order to derive the special parameter of account for motion pattern at large, what conform with purpose is periodically position curve to be divided into sequence according to it.Some parameter is tried to achieve by an one sequence.Such parameter for example is an amplitude, swing persistent period and perpendicular to the distance of passing by on the swaying direction (going on foot wide).
Because it is not periodicity on the strict mathematical meaning according to existing understanding that human body is swung inherent periodicity, each sequence is inequality, and only is proximate.What conform with purpose is to be attached to a parameter of being tried to achieve by single sequence (" single step " analyze) to go up or replace it and tried to achieve a relevant parameters and provided assembly average and standard deviation by a plurality of sequences.Derive conclusion by this way about the human body swaying movement regularity.
Conforming with destination about the quantitative statement of human body swaying movement regularity obtains by amplitude distribution and/or the frequency distribution of obtaining at least one position curve.So specially by means of frequency spectrum analysis method, for example so-called " fast Fourier transformation " derives frequency distribution as parameter from data set.
The centre-of-gravity motion of human body is used as another useful parameter and obtains.Calculate distance between corresponding sign original position and final position from certain or each data item of data set for this reason.Can determine for example human body deflection angle thus.Obtain parameter by means of the mutual relative position of sign in addition, they represent the orientation of each position of human body in the space and their mutual positions, particularly the position of head and shoulder.These parameters mainly are human body rotation and the distortion angles of jolting, i.e. head moving with respect to both shoulders.The length of certain or each position curve and total distance that corresponding sign is passed by are used as parameter and obtain in addition.
Be the pattern comparison of form with the graphic element between the pattern of deriving by means of pattern of deriving and reference data set by storage by the real time data group, especially by means of (nerve) fuzzy logic or a neutral net, effective and efficient manner and method are determined the corresponding degree of consistency simply and especially, and for to arrange corresponding graphic element and/or other parameter by differential diagnosis.Conform with destination and replenish reference data set in the mode of self-training by corresponding real time data group.And judge corresponding the measurement corresponding to the diagnosis that the parameter that each reference data set is used for corresponding pathology figure or pattern can be used for afterwards.Sign of trying to achieve by means of the concordance of data set and a plurality of reference data set can also be made it corresponding to each the probability among a plurality of reason figure that diagnose a disease in advance.
The present invention finishes like this about the task of proposing device: in order to judge the device of one stature-trunk-kinetic pattern, this device has a plurality of marker generators that carry and a receiving equipment that comprises two receivers of at right angles laying mutually on measured's head and health, described receiving equipment is in order to write down the position curve of each marker generator, also has a data handling equipment that is connected with receiving equipment, wherein said data handling equipment comprises one in order to the processing level from the data set of the calculated signals correspondence position curve of receiving equipment, a data base who comprises a plurality of reference data set, one in order to be obtained parameter and to be obtained the analysis module of reference value by reference data set by data set, and a comparison module, this comparison module is obtained the degree of consistency between data set and reference data set by means of parameter and reference value.
The data handling equipment that is connected with the receiving equipment of each sign of record has a processing level that goes out to represent the data set of position curve in order to the calculated signals from receiving equipment.This can be the ultrasound emission and the receiving equipment of discrete setting on a plurality of spaces, and CCD (charge-coupled image sensor) video camera (video camera) image unit or other are in order to record and processing sound or optical signal in case of necessity.Respective receiver conforms with destination mutually at right angles installs, and makes at least two the different planes that are recorded in of mark position curve for example to carry out on XY-plane and YZ-plane or the XZ-plane.Position curve can be calculated by the measurement data of two receivers with respect to the 3rd planar coordinate.In that as a token of measure during the source with ultrasonic replacement light also can be bright indoor carrying out.
Be configured in a data base who handles the level back and be used for the reference data stored group, it is preferably tried to achieve in reference measure repeatedly.The analysis module of data handling equipment or AG are obtained a plurality of parameters or reference value from the data set of real-time collecting and corresponding its reference data set, and they obtain the degree of consistency between the data set by means of parameter and reference value with the method for pattern identification in a comparison module or comparative degree.Then data handling equipment delivers to the data base corresponding to the sign of a pathology figure and by means of sign with data set for one of each data set configuration, to expand corresponding reference data set.
Handling level distributes the position curve of each sign to conform with purpose as data item to data set.Obtain a matrix like this, and each data item is to be engraved in the three-dimensional coordinate that flute deck mark is fastened when corresponding with number and the corresponding a plurality of data item of conventional number.Handle the memorizer that level connects a temporal data group after preferably, the measurement data of collecting is carried out intermediate storage with opposing.
Advantage of the present invention mainly is, the computer-made decision of being undertaken by the measurement data that the kinetic pattern of being collected by a plurality of optics or acoustic method is tried to achieve and can be drawn about the conclusion based on which pathology figure of fault correspondence of corresponding sports pattern by means of the explanation of relevant parameter by the curve stroke of its derivation and function pattern.When finishing test data that reality do not contact measured's human body and collecting, one with the isolating data handling equipment of measured's human body in finish judgement, measurement data is processed outside human body in this equipment, and discerns for pattern and to get ready.
By unconspicuous measured of disease or the tangible measured of disease mainly is that head-health-kinetic pattern judges that typical pathology figure makes the following possibility that becomes: the scientific method with a plurality of reference datas and reference pattern can be used to set up corresponding relation between real-time collecting and kinetic pattern of not diagnosed and known pathology figure.The parameter of trying to achieve by means of judgement can be made about psychology like this, the conclusion of spirit and/or neural fault, for example schizophrenia, depression and parkinson.
Describe the embodiment of the invention in detail by accompanying drawing below, in the accompanying drawing;
Fig. 1 has the device sketch map of the parts that are used for judging kinetic pattern,
The kinetic pattern that produces during Fig. 2 measured walking, it is represented by the position curve of 4 sign projections on the XY-plane;
The kinetic pattern that produces when Fig. 3 measured stands, its method for expressing be with among Fig. 2, and
Kinetic pattern that is changed owing to seance by diagnosis pathology figure of Fig. 4.
Two receivers 2 that at right angles are orientated mutually are in order to collect measured 1 kinetic pattern in Fig. 1.Receiver receives from a plurality of marker generator M with measured's 1 motion with mode and the method that does not have to describe in detail
iSignal.Manifesting of human motion can be implemented on the optical track especially simply.Adopt electric filament lamp or light emitting diode generator M as a token of in the case
i, and correspondingly use video camera, for example video camera is as receiver 2.The sign of kinetic pattern produces also available ultrasonic transmitter generator M as a token of
iWith with ultrasonic receiver as receiver 2.Also can use passive marker generator M in addition
i, it just reflects the signal that sends from external source.As common so-called skull-health figure, conform with destination's restriction and observe in measured 1 the head and the motion of trunk.Therefore on a left side of measured 1 shoulder and right shoulder, respectively have a marker generator M
1And M
2, respectively having another marker generator M on its volume and on the hindbrain
3And M
4
Receiver 2 is respectively with a marker generator M
iThe X-Y scheme of motion is given the processing level 10 that is included in the data handling equipment 3, and the figure that it is transmitted by receiver 2 is obtained each marker generator M in three dimensions
iThe position curve m of t in time
iEach position curve m
iAir coordinates to be illustrated in Cartesian Coordinate be X, Y, among the Z, and measured 1 homeposition is set to zero point, lateral axis corresponds to X-axis.Y direction is the horizontal direction of measured's 1 walking, and Z vertically upward.The basic plane that is made of corresponding axis is XY plane (horizontal plane), YZ plane (longitudinal vertical plane) and ZX plane (side direction vertical).
Data handling equipment 3 is finished each marker generator M by means of an algorithm handling level 10
iPosition curve m
iCalculating.If receiver 2 adopts the analog record technology, handle level 10 conversion of at first carrying out from the analog data to the numerical data.Handle level 10 with position curve m
iSend one to as data set DS and be preferably temporary data storage 11.Wherein data set DS is divided into data item DF
i, a data item DF
iRepresent a marker generator M
iPosition curve m
iData storage 11 provides data set DS to give analysis module 12 that form of software realizes for its use.Analysis module 12 is at first by selecting data to generate position curve m from data set DS
iIn basic plane X Y, the projection on YZ and the ZX.Because position curve m
iGenerally have the periodic structure that is caused by the human body swing, the algorithm of analysis module 12 is then divided position curve m
iBe periodic sequence.The initial sum of such sequence stops by position curve m
iA sharp-pointed direction change and identify, the one-period of an accurate corresponding human body swing of sequence.By being projected in basic plane X Y, YZ and ZX go up and are divided into the position curve m of sequence
i, the algorithm of analysis module 12 is further derived a plurality of parameter K G.
On the one hand by position curve m
iGeometry and the measurement derived parameter KG of physics.Based on each position curve m
iPeriodic structure, relevant parameter KG mainly is the amplitude of a swing, persistent period in cycle, the distance (going on foot wide) of passing by perpendicular to swaying direction in frequency and hunting period.Can derive these parameter K G (single step analysis) from single sequence, also can derive them and provide meansigma methods and standard deviation (full response analysis) from a plurality of sequence statistics.The scrambling of human body swing further is quantized by the frequency distribution that provides distribution of amplitudes and draw by sub-analysis of spectrum (fourier transform).Obtain physical parameter KG by the motion of the emphasis of human body in addition, and obtain human body with respect to the rotation of spatial rotation and head with respect to trunk.Need make up a plurality of sign M for this reason
iPosition curve m
i
On the other hand, also obtain curve form position curve m except feature outer analysis modules 12 physics and/or how much
iThe degree of consistency with the comparison pattern of storing as graphic element.With the comparison of a graphic element can be to single sequence or whole position curve m
iCarry out.Wherein swing sequence is described by means of the shape of its break over region.Typical graphic element has arcual, annular or pointed break over region.In order to whole position curve m to be described
iGraphic element but with position curve m
iThe contour shape on perspective plane as its feature.They with the graphic element of the pattern (for example butterfly) that is shaped as geometric figure (for example triangle, square) or more complicated relatively.Analysis module 12 will be sent to comparison module 13 by the parameter K G that data set DS derives.
For assessment data group DS, provide reference data set RS to use to analysis module 12 from data base 14.Reference data set RS has the related data structure with data set DS, yet add a sign K corresponding to a pathology figure is arranged
RSAnalysis module 12 is obtained a plurality of reference value RG by reference data set RS, and it is according to the parameter K G of its derivation corresponding to data set DS.Reference value RG sends comparison module 13 in case of necessity to after derivation.Reference data set RS is based upon one and simulates in the reference measure of setting up data set DS and finish.The sign K of corresponding reference data set RS
RSBe sent to comparison module 13 with reference value RG.In comparison module 13, obtain the degree of consistency of data set DS and reference data set RS by means of parameter K G and reference value RG.By the graphic element of typical pathology figure is set, by means of direct pattern relatively, real-time kinetic pattern is directly finalized the design or at least by qualitative classification.Support quantitative analysis based on further investigation to parameter K G.
The pathology characteristic of measured 1 kinetic pattern always is superimposed with individual character.The unit of kinetic pattern or sign, thereby and each parameter can be the same as or similar to different pathology figure.The test of doing on this external measured 1 can not strictly reappear.Therefore parameter K G not only, and reference value RG is unsharp with respect to its concluding disease of examining in advance.In order to carry out this unsharp calculating, comparison module 13 is preferably based on so-called fuzzy logic work.According to fuzzy logic ordination, a parameter K G or a reference value RG who provides as the measured value of strictness is become unsharp (obfuscation) artificially.By unsharp parameter K G and unsharp reference value RG are compared, in comparison module 13, try to achieve the classification factor TF of the reflection degree of consistency.Classification factor TF has reflected because obfuscation and the pathology concordance of reinforced data set DS and reference data set RS.Individual character and details that can not reappear is filtered by unsharp investigation of doing by fuzzy logic in the kinetic pattern.It reflected with then de-fuzzy after the corresponding to probability of reference data set RS.
For data set DS being obtained sign K corresponding to pathology figure
DS, comparison module 13 compares with data set DS and from a plurality of reference data set RS that take out from data base 14 with said method.The classification factor TF that obtains when data set DS of each one comparison and a reference data set RS then is stored and is used to obtain sign K
DSIn order to try to achieve sign K
DS, application examples is as a neutral net here.
For output identification K
DS, comparison module 13 connects an output module 20, and it for example is a display, a printer or a drawing apparatus.Output module 20 also can provide the kinetic pattern of storing with data set DS form in the data storage 11.Then import control 21 and provide corresponding sign K to data set by means of the data base
DS, and according to this sign K
DSBy the scientific method of self-training data set is delivered to data base 14 as with reference to data set RS.
Fig. 2 is by means of marker generator M
1To M
4Position curve m
1To m
4Projection on horizontal plane XY illustrates the typical motion pattern that a walking of carrying out according to the Unterberger/Fukuda method is tested.The measured whips up one section walking distance in such paces test.Usually measured 1 is muffled eyes for this reason, to avoid visual orientation in the space.Position curve m
iPresent clearly wavy periodicity, it be by when walking human body center of gravity move and cause.The sequence 31 expression measured's 1 that limit by two adjacent turning points 30 a walking period, it has two steps in succession, and this is shown in position curve m among Fig. 2 for example
1On.
Typical mode is that a unobtrusive measured 1 also moves one apart from l in the walking test on longitudinal axis Y direction
1Gravity center of human body's laterally offset l
2Perhaps the amplitude a of sideshake is classified as pathology when surpassing (critical) threshold value.The result that the position curve of Fig. 2 and aforementioned a series of graphic element relatively obtain is the concordance that height is arranged with a pointed comparison pattern.
Fig. 3 illustrates same measured's 1 kinetic pattern.Yet this kinetic pattern generates by the Romberg method in the test of standing.This moment the measured 1 about 1 minute clock time of standing, and usually where necessary eyes muffled.The kinetic pattern that produces in the test of standing is equivalent to the twisted circular motion of a confusion, and it has the amplitude littler than the walking test (among Fig. 3 the amplification of used figure scale ratio Fig. 2 10 times).The position curve m that the test of standing shown in Figure 3 produces
iPreferably describe with graphic element with its integral body.For these position curves m
i, its contour shape has concordance highly with the right angled triangle 32 shown in the example of passing the imperial examinations at the provincial level at Fig. 3.
The method also can be used as the inspection method of following treatment the pathology figure except that being used to discern.Fig. 4 illustrates repeatedly the record of suffering from the walking test that schizoid patient does on one's body at, and wherein at Fig. 4 a, the kinetic pattern shown in 4b and the 4c is each 30 days record at interval.The diagnosis and treatment achievement that obtains during treating is presented on from Fig. 4 a to Fig. 4 c position curve m
iThe bending on the left side reduce one by one.Therefore this method checks that as measuring method patient is particularly advantageous to the reaction of medicine.
Label list
1 measured, 2 receivers, 3 data processing equipments, 10 process level, 11 data storages, 12 analysis modules, 13 comparison modules, 20 output modules, 21 database input controls, 30 turning points, 31 sequences, 32 triangles as graphic element
m
iPosition curve, DS data group, DFiData item, KG parameter, RS reference data set, KRSThe sign of reference data set, RG reference value, TF classified adaptive factor, KDSThe sign of data group, X lateral axis, Y longitudinal axis, Z vertical axis, XY horizontal plane, YZ longitudinal vertical plane, ZX side direction vertical plane, l1The longitudinal center of gravity motion, l2The laterally offset a sideshake amplitude of center of gravity
Claims (18)
1. in order to judge the method for one stature-trunk-kinetic pattern, this pattern is the marker generator (M that moves with measured's (1) human body by means of a plurality of
i) record, wherein,
-each marker generator (M
i) position curve (m
i) (t) is collected in time in three dimensions, and as one to all marker generator (M
i) data item (DF of common data set (DS)
i) be stored,
-kinetic pattern is described by means of the parameter (KG) that is derived by data set (DS), and
-each parameter (KG) is compared with a reference value (RG) by the corresponding derivation of a stored reference data set (RS), and
-parameter or each parameter (KG) are by at least one position curve (m
i) (ZX) projection on the plane in is derived for XY, YZ at three datum planes of Cartesian Coordinate system.
2. the method for claim 1 is characterized in that, by at least one position curve (m
i) obtain the swing of corresponding human body one hunting period sequence, and derive at least one parameter (KG) by a single sequence.
3. method as claimed in claim 2 is characterized in that, is obtained the meansigma methods and the standard deviation of parameter (KG) by a plurality of sequences.
4. as each described method in the claim 1 to 3, it is characterized in that, from least one position curve (m
i) periodicity obtain the distribution of amplitudes of human body swing and/or frequency distribution as parameter (KG).
5. the method for claim 1 is characterized in that, by at least one marker generator (M
i) original position and the distance (l between final position
1, l
2) obtain the centre-of-gravity motion of human body as parameter (KG).
6. the method for claim 1 is characterized in that, by means of marker generator (M
i) the position obtain at least one human body in spatial orientation as parameter (KG).
7. the method for claim 1 is characterized in that, by means of marker generator (M
i) the position obtain the mutual position of human body as parameter (KG).
8. the method for claim 1 is characterized in that, at least one position curve (m
i) length be used as parameter (KG) and obtain.
9. the method for claim 1 is characterized in that, a pattern and a position curve (m as the graphic element storage
i) the shape of at least a portion between the degree of consistency obtained as parameter (KG).
10. the method for claim 1 is characterized in that, obtains the classification factor (TF) of the degree of consistency between expression data set (DS) and the reference data set (RS) by means of fuzzy logic.
11. the method for claim 1 is characterized in that, to each reference data set (RS) that is stored in data base (14) a sign (K corresponding to a pathology figure is set
RS), and obtain the sign (K of data set (DS) by means of the degree of consistency of data set (DS) and a plurality of reference data set (RS)
DS).
12. method as claimed in claim 11 is characterized in that, obtains sign (K by means of neutral net
DS).
13. as claim 11 or 12 described methods, it is characterized in that, according to the method for self-training and by means of its sign (K
DS) each data set (DS) is delivered to data base (14).
14. implement the device of the described method of claim 1, this device has a plurality of marker generator (M that carry on measured's head and health
i) and a receiving equipment (2) that comprises two receivers of at right angles laying mutually (2), described receiving equipment is in order to write down each marker generator (M
i) position curve (m
i), also have a data handling equipment (3) that is connected with receiving equipment (2), it is characterized in that described data handling equipment comprises
-one in order to the calculated signals correspondence position curve (m from receiving equipment (2)
i) the processing level (10) of data set (DS),
-one data base (14) who comprises a plurality of reference data set (RS),
-one in order to being obtained parameter (KG) and to obtain the analysis module (12) of reference value (RG) by reference data set (RS) by data set (DS), and
-one comparison module (13), this comparison module is obtained the degree of consistency between data set (DS) and reference data set (RS) by means of parameter (KG) and reference value (RG).
15. device as claimed in claim 14 is characterized in that, handles level (10) and distributes to each marker generator (M of data set (DS)
i) position curve (m
i) as data item (DF
i).
16. as claim 14 or 15 described devices, it is characterized in that, connect the data sets memories (11) of a temporary usefulness after the processing level (10).
17. device as claimed in claim 14 is characterized in that, comparison module (13) distributes the sign (K corresponding to a pathology figure for each data set (DS)
DS) and by means of sign (K
DS) data feed group (DS) gives data base (14).
18. device as claimed in claim 14 is characterized in that, data handling equipment (3) is presented each parameter (KG) and is given an output module (20), and it is in order to show kinetic pattern or reference pattern.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/EP1998/001341 WO1999044502A1 (en) | 1998-03-07 | 1998-03-07 | Method and device for evaluating an kinetic pattern |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1284844A CN1284844A (en) | 2001-02-21 |
| CN1208025C true CN1208025C (en) | 2005-06-29 |
Family
ID=8166894
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB988138417A Expired - Fee Related CN1208025C (en) | 1998-03-07 | 1998-03-07 | Method and device for evaluating an kinetic pattern |
Country Status (12)
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| US (1) | US6473717B1 (en) |
| EP (1) | EP1061851B1 (en) |
| JP (1) | JP2002505134A (en) |
| KR (1) | KR100499550B1 (en) |
| CN (1) | CN1208025C (en) |
| AT (1) | ATE213396T1 (en) |
| AU (1) | AU761085B2 (en) |
| CA (1) | CA2322638C (en) |
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| DK (1) | DK1061851T3 (en) |
| ES (1) | ES2172142T3 (en) |
| WO (1) | WO1999044502A1 (en) |
Families Citing this family (44)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002095534A2 (en) * | 2001-05-18 | 2002-11-28 | Biowulf Technologies, Llc | Methods for feature selection in a learning machine |
| DE19918008A1 (en) * | 1999-04-21 | 2000-10-26 | Claussen Claus Frenz | Method to determine neck movement pattern of subject; involves placing markers on head, neck and shoulders and storing locus curve of each marker in 3D space as function of time |
| DE10008251A1 (en) * | 2000-02-23 | 2001-08-30 | Gordon Pipa | Method for detecting and analyzing complex correlations between signals, greatly reduces the computing power required to find such correlations |
| US7624074B2 (en) * | 2000-08-07 | 2009-11-24 | Health Discovery Corporation | Methods for feature selection in a learning machine |
| US20030062675A1 (en) * | 2001-09-28 | 2003-04-03 | Canon Kabushiki Kaisha | Image experiencing system and information processing method |
| FR2832916B1 (en) * | 2001-11-30 | 2004-11-26 | Marie Madeleine Renee Bernard | AUTOMATED VISION CONFERENCE GONIOMETER |
| US7409373B2 (en) | 2001-12-28 | 2008-08-05 | Concepta Ab | Pattern analysis system and method |
| SE525655C2 (en) * | 2001-12-28 | 2005-03-29 | Bergfalk & Knagenhjelm Ab | Pattern analysis system and method |
| JP4054585B2 (en) * | 2002-02-18 | 2008-02-27 | キヤノン株式会社 | Information processing apparatus and method |
| EP1349114A3 (en) * | 2002-03-19 | 2011-06-15 | Canon Kabushiki Kaisha | Sensor calibration apparatus, sensor calibration method, program, storage medium, information processing method, and information processing apparatus |
| US20040215074A1 (en) * | 2003-04-24 | 2004-10-28 | Vincenzo Giuliano | Imaging and scoring method for cervical spinal impairment using magnetic resonance imaging |
| US9826537B2 (en) | 2004-04-02 | 2017-11-21 | Rearden, Llc | System and method for managing inter-cluster handoff of clients which traverse multiple DIDO clusters |
| US8654815B1 (en) | 2004-04-02 | 2014-02-18 | Rearden, Llc | System and method for distributed antenna wireless communications |
| US10277290B2 (en) | 2004-04-02 | 2019-04-30 | Rearden, Llc | Systems and methods to exploit areas of coherence in wireless systems |
| US10425134B2 (en) | 2004-04-02 | 2019-09-24 | Rearden, Llc | System and methods for planned evolution and obsolescence of multiuser spectrum |
| US9819403B2 (en) | 2004-04-02 | 2017-11-14 | Rearden, Llc | System and method for managing handoff of a client between different distributed-input-distributed-output (DIDO) networks based on detected velocity of the client |
| JPWO2005096939A1 (en) * | 2004-04-07 | 2008-02-28 | 財団法人新産業創造研究機構 | Rehabilitation support measurement system and rehabilitation support measurement method |
| US20060055706A1 (en) * | 2004-09-15 | 2006-03-16 | Perlman Stephen G | Apparatus and method for capturing the motion of a performer |
| US8194093B2 (en) * | 2004-09-15 | 2012-06-05 | Onlive, Inc. | Apparatus and method for capturing the expression of a performer |
| US7566346B2 (en) * | 2004-10-29 | 2009-07-28 | X-Spine Systems, Inc. | Prosthetic implant and method |
| DE102004060602B4 (en) * | 2004-12-08 | 2009-08-06 | Eberhard-Karls-Universität Tübingen Universitätsklinikum | System and method for determining a movement category and its degree of manifestation |
| US7633521B2 (en) * | 2005-02-25 | 2009-12-15 | Onlive, Inc. | Apparatus and method improving marker identification within a motion capture system |
| US7605861B2 (en) * | 2005-03-10 | 2009-10-20 | Onlive, Inc. | Apparatus and method for performing motion capture using shutter synchronization |
| US8659668B2 (en) | 2005-10-07 | 2014-02-25 | Rearden, Llc | Apparatus and method for performing motion capture using a random pattern on capture surfaces |
| GB2438167A (en) | 2006-05-19 | 2007-11-21 | Univ Teesside | Balance monitor |
| US7567293B2 (en) * | 2006-06-07 | 2009-07-28 | Onlive, Inc. | System and method for performing motion capture by strobing a fluorescent lamp |
| US20100231692A1 (en) * | 2006-07-31 | 2010-09-16 | Onlive, Inc. | System and method for performing motion capture and image reconstruction with transparent makeup |
| DE102007013566B4 (en) * | 2007-03-21 | 2017-02-23 | Siemens Healthcare Gmbh | Method for image data acquisition and medical modality |
| JP5388024B2 (en) * | 2008-11-13 | 2014-01-15 | 国立大学法人 千葉大学 | Posture control function diagnostic device |
| JP2010216968A (en) * | 2009-03-16 | 2010-09-30 | Fuji Xerox Co Ltd | System and program for measuring position |
| US9901828B2 (en) | 2010-03-30 | 2018-02-27 | Sony Interactive Entertainment America Llc | Method for an augmented reality character to maintain and exhibit awareness of an observer |
| WO2011123932A1 (en) * | 2010-04-06 | 2011-10-13 | Nelson Greenberg | Virtual exerciser device |
| US8331670B2 (en) * | 2011-03-22 | 2012-12-11 | Konica Minolta Laboratory U.S.A., Inc. | Method of detection document alteration by comparing characters using shape features of characters |
| CN104159507B (en) * | 2012-03-16 | 2016-10-12 | 富士通株式会社 | Device for detecting rock and rock detection method |
| US11189917B2 (en) | 2014-04-16 | 2021-11-30 | Rearden, Llc | Systems and methods for distributing radioheads |
| US9973246B2 (en) | 2013-03-12 | 2018-05-15 | Rearden, Llc | Systems and methods for exploiting inter-cell multiplexing gain in wireless cellular systems via distributed input distributed output technology |
| US10488535B2 (en) | 2013-03-12 | 2019-11-26 | Rearden, Llc | Apparatus and method for capturing still images and video using diffraction coded imaging techniques |
| US9923657B2 (en) | 2013-03-12 | 2018-03-20 | Rearden, Llc | Systems and methods for exploiting inter-cell multiplexing gain in wireless cellular systems via distributed input distributed output technology |
| RU2767777C2 (en) | 2013-03-15 | 2022-03-21 | Риарден, Ллк | Systems and methods of radio frequency calibration using the principle of reciprocity of channels in wireless communication with distributed input - distributed output |
| JP6403058B2 (en) * | 2014-10-29 | 2018-10-10 | 株式会社マイソフト | Reaction range measuring apparatus and program |
| US11589783B2 (en) * | 2015-05-26 | 2023-02-28 | Rajneesh Bhandari | Apparatus and method for diagnosing vertigo and balance related ailment |
| EP3477656A1 (en) * | 2017-10-28 | 2019-05-01 | Tata Consultancy Services Limited | Systems and methods for quantification of postural balance of users in an augmented reality environment |
| AU2021277244A1 (en) * | 2020-05-20 | 2022-10-27 | Bon Secours Mercy Health, Inc. | Head and neck full motion testing system |
| CN112754435A (en) * | 2021-01-20 | 2021-05-07 | 山东易听泰医疗技术有限公司 | Futian fine stepping experimental instrument |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4631676A (en) * | 1983-05-25 | 1986-12-23 | Hospital For Joint Diseases Or | Computerized video gait and motion analysis system and method |
| US4813436A (en) * | 1987-07-30 | 1989-03-21 | Human Performance Technologies, Inc. | Motion analysis system employing various operating modes |
| US5086404A (en) * | 1988-09-02 | 1992-02-04 | Claussen Claus Frenz | Device for simultaneous continuous and separate recording and measurement of head and body movements during standing, walking and stepping |
| DE3829885A1 (en) * | 1988-09-02 | 1990-03-15 | Claussen Claus Frenz Prof Dr M | Device for continuously performed, separate recording and measurement of head and trunk movements during standing and locomotion |
| JPH05500466A (en) * | 1990-03-30 | 1993-02-04 | ウィプラッシュ・アナリシス・インコーポレイテッド | Non-invasive method and device for measuring tibia motor function |
| US5203346A (en) * | 1990-03-30 | 1993-04-20 | Whiplash Analysis, Inc. | Non-invasive method for determining kinematic movement of the cervical spine |
| US5823958A (en) * | 1990-11-26 | 1998-10-20 | Truppe; Michael | System and method for displaying a structural data image in real-time correlation with moveable body |
| DE69132952T2 (en) * | 1990-11-30 | 2002-07-04 | Sun Microsystems Inc | COMPACT HEAD TRACKING SYSTEM FOR CHEAP VIRTUAL REALITY SYSTEM |
| US6223064B1 (en) * | 1992-08-19 | 2001-04-24 | Lawrence A. Lynn | Microprocessor system for the simplified diagnosis of sleep apnea |
| US5562104A (en) * | 1994-04-01 | 1996-10-08 | Movemap, Inc. | Measuring movement disorder |
| US5961923A (en) * | 1995-04-25 | 1999-10-05 | Irori | Matrices with memories and uses thereof |
| US6079862A (en) * | 1996-02-22 | 2000-06-27 | Matsushita Electric Works, Ltd. | Automatic tracking lighting equipment, lighting controller and tracking apparatus |
| US6056703A (en) * | 1996-04-03 | 2000-05-02 | Rush Presbyterian-St Luke's Medical Center | Method and apparatus for characterizing gastrointestinal sounds |
| US6213958B1 (en) * | 1996-08-29 | 2001-04-10 | Alan A. Winder | Method and apparatus for the acoustic emission monitoring detection, localization, and classification of metabolic bone disease |
| US5963891A (en) * | 1997-04-24 | 1999-10-05 | Modern Cartoons, Ltd. | System for tracking body movements in a virtual reality system |
| US5891060A (en) * | 1997-10-13 | 1999-04-06 | Kinex Iha Corp. | Method for evaluating a human joint |
| US5954674A (en) * | 1997-10-13 | 1999-09-21 | Kinex Iha Corporation | Apparatus for gathering biomechanical parameters |
| US5991701A (en) * | 1997-10-13 | 1999-11-23 | Kinex Iha Corp. | Method for improved instantaneous helical axis determination |
-
1998
- 1998-03-07 DE DE59803158T patent/DE59803158D1/en not_active Expired - Fee Related
- 1998-03-07 AU AU75204/98A patent/AU761085B2/en not_active Ceased
- 1998-03-07 DK DK98922614T patent/DK1061851T3/en active
- 1998-03-07 DE DE19882283T patent/DE19882283D2/en not_active Expired - Lifetime
- 1998-03-07 KR KR10-2000-7009872A patent/KR100499550B1/en not_active IP Right Cessation
- 1998-03-07 JP JP2000534115A patent/JP2002505134A/en active Pending
- 1998-03-07 WO PCT/EP1998/001341 patent/WO1999044502A1/en active IP Right Grant
- 1998-03-07 CA CA002322638A patent/CA2322638C/en not_active Expired - Fee Related
- 1998-03-07 AT AT98922614T patent/ATE213396T1/en not_active IP Right Cessation
- 1998-03-07 ES ES98922614T patent/ES2172142T3/en not_active Expired - Lifetime
- 1998-03-07 CN CNB988138417A patent/CN1208025C/en not_active Expired - Fee Related
- 1998-03-07 EP EP98922614A patent/EP1061851B1/en not_active Expired - Lifetime
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2000
- 2000-09-07 US US09/657,075 patent/US6473717B1/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN1284844A (en) | 2001-02-21 |
| DE19882283D2 (en) | 2004-12-23 |
| DK1061851T3 (en) | 2002-05-13 |
| ES2172142T3 (en) | 2002-09-16 |
| CA2322638A1 (en) | 1999-09-10 |
| AU7520498A (en) | 1999-09-20 |
| CA2322638C (en) | 2005-08-09 |
| DE59803158D1 (en) | 2002-03-28 |
| ATE213396T1 (en) | 2002-03-15 |
| WO1999044502A1 (en) | 1999-09-10 |
| EP1061851B1 (en) | 2002-02-20 |
| JP2002505134A (en) | 2002-02-19 |
| EP1061851A1 (en) | 2000-12-27 |
| KR20010041665A (en) | 2001-05-25 |
| KR100499550B1 (en) | 2005-07-07 |
| US6473717B1 (en) | 2002-10-29 |
| AU761085B2 (en) | 2003-05-29 |
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